The Microsoft Azure Machine Learning service makes it easier to create predictive analytics solutions, connect them to other Azure products, and publish them as APIs

Most every software and platform developer is hurrying to offer better ways to build machine learning and predictive analytics (no wonder those skills are skills are in demand). Microsoft, never one to pass up a gold rush, is prepping a solution that's a service for Azure, not stand-alone software.

Microsoft Azure Machine Learning, or Azure ML, is a way to build cloud-based predictive analytics solutions that can be assembled from templates and common workflows, rather than written from scratch by a programmer or data scientist. The solutions can then be published as APIs and consumed by other Azure services or third-party applications.

Predictive analytics involves looking at trends across reams of gathered data to find useful insights, such as whether to keep a given item in stock more often on the weekends, or whether credit card charges from a particular vendor may be fraudulent. But building predictive analytics systems isn't a snap. It requires both programming savvy and data-analysis chops.

The prebuilt Azure ML templates created by Microsoft cover standard machine-learning functions, such as decision-tree algorithms or recommendation systems. They can be used as-is, or they can be extended by a savvy data scientist by way of the R language. Support for Python, another language in broad use by data scientists (and in science work generally), is slated to be added in the future.

Azure ML stands in contrast with IBM's Watson service, which is far more of a moonshot project looking to attract an equally ambitious class of developers. Microsoft built Azure ML to meet the more immediate, quotidian needs of businesses that want to create consumable analytics services.

Microsoft has already made noises about becoming your one-stop analytics shop with SQL Server 2014, the existing Hadoop-powered analytics components of Azure (HDInsight), and Azure's data-capturing platform Intelligent Systems Service. To that mix, Azure ML adds a set of prebuilt solutions to commonly executed analytics problems that can be implemented without having to write code or hire a data scientist to pull it off.

Data scientists will still be valuable for understanding and interpreting analytics, as InfoWorld's David Linthicum noted, but Azure ML seems less about replacing expertise entirely and more about doing away with a lot of the boilerplate programming and by-rote heavy lifting involved in creating analytics.